Keywords: Image Reconstruction, Parallel Imaging, Neural ODEIn parallel MRI, DNN-based models have recently outperformed conventional reconstruction techniques and can reconstruct high-quality MRI images, especially at high acceleration factors. We propose a model-based neural ODE network to reconstruct artifact-free MR images from under-sampled k-space data. We replaced the existing U-Net with a modified U-Net framework using neural ODEs with E2E-VarNet as the backbone. Our network solves unrolled iterations of reconstruction optimization with neural ODEs, and each neural ODE uses a gradient update step as a dynamics step. Our approach showed the improved reconstruction performance comparable to the SOTA method with few parameters.
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